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DOI: 10.4121/e343331b-496f-40ab-83eb-f546df6dffa6

Koo, Ja-Ho; Edo Abraham; Jonoski, Andreja; Solomatine, Dimitri (2025): Codes underlying: Comparison of scenario reduction approaches for reservoir inflow scenarios generated by a Bayesian Neural Network. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/e343331b-496f-40ab-83eb-f546df6dffa6.v1

Categories

• Civil Engineering
• Land and Water Management
• Engineering
• Environment

Keywords

BNN energy distance Euclidian distance Manhattan distance scenario generation scenario reduction Wasserstein distance

Geolocation

view on openstreetmap

Time coverage

2001-2020

Licence

CC BY 4.0

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by Ja-Ho Koo , Edo Abraham, Andreja Jonoski , Dimitri Solomatine

The data set and codes for a paper, Comparison of scenario reduction approaches for reservoir inflow scenarios generated by a Bayesian Neural Network.

Including reservoir inflow data for the Daecheong reservoir in South Korea, there are codes to build a BNN model with hyperparameter optimization using the TPE algorithm. In addition, codes for scenario reduction by four different measures, Wasserstein, energy, Euclidean, and Manhattan distances, are integrated.

History

• 2025-03-25 first online, published, posted

Publisher

4TU.ResearchData

Format

.xlsx, .py, .txt

Derived from

• https://www.kwater.or.kr

Organizations

IHE Delft, Department of Hydroinformatics and Socio-Technical Innovation
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Water Management
Korea Water Resources Public Corporation (K-water)